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Science as instrumentation. The case for psychiatric rating scales

Author

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  • Philippe Moigne

    (Université Paris V, Sorbonne Paris Cité
    Université Paris 5-René Descartes)

  • Pascal Ragouet

    (Université Segalen Bordeaux 2)

Abstract

The aim of this article is to test the model analysis conceived by Terry Shinn on the autonomy and unity of science. For him, the differentiation of sciences can be explained in a large part by the diffusion of generic instruments created by research-technologists moving in interstitial arenas between higher education, industry, statistics institutes or the military. We have applied this analysis to research on depression by making the hypothesis that psychiatric rating scales could have played a similar role in the development of this scientific field. To that purpose, we proceeded to a lexicographic study of keywords mentioned in articles listed by the PsycINFO© data base on this subject between 1950 and 2000. In order to realize an associated words analysis, we constructed a co-occurrence matrix and used clustering analysis based on a grouping index; that is, the equivalency index. We obtained significant aggregates of keywords associated with significant periods, or major moments, of the development of research on depression. This periodization confirmed the structural role played by psychiatric rating scales in the development of this scientific field, and led us to discuss and to extend some elements of the model initiated by Shinn.

Suggested Citation

  • Philippe Moigne & Pascal Ragouet, 2012. "Science as instrumentation. The case for psychiatric rating scales," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 329-349, November.
    • Handle: RePEc:spr:scient:v:93:y:2012:i:2:d:10.1007_s11192-012-0673-1
    DOI: 10.1007/s11192-012-0673-1
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    References listed on IDEAS

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    1. Jouko Miettunen & Pentti Nieminen, 2003. "The effect of statistical methods and study reporting characteristics on the number of citations: A study of four general psychiatric journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 57(3), pages 377-388, July.
    2. Neal Coulter & Ira Monarch & Suresh Konda, 1998. "Software engineering as seen through its research literature: A study in co‐word analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(13), pages 1206-1223.
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    1. W. W. Koczkodaj & T. Kakiashvili & A. Szymańska & J. Montero-Marin & R. Araya & J. Garcia-Campayo & K. Rutkowski & D. Strzałka, 2017. "How to reduce the number of rating scale items without predictability loss?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 581-593, May.

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